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Interactive analysis of single-cell data using flexible workflows with SCTK2
Analysis of single-cell RNA sequencing (scRNA-seq) data can reveal novel insights into the heterogeneity of complex biological systems. Many tools and workflows have been developed to perform different types of analyses. However, these tools are spread across different packages or programming enviro...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10436054/ https://www.ncbi.nlm.nih.gov/pubmed/37602214 http://dx.doi.org/10.1016/j.patter.2023.100814 |
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author | Wang, Yichen Sarfraz, Irzam Pervaiz, Nida Hong, Rui Koga, Yusuke Akavoor, Vidya Cao, Xinyun Alabdullatif, Salam Zaib, Syed Ali Wang, Zhe Jansen, Frederick Yajima, Masanao Johnson, W. Evan Campbell, Joshua D. |
author_facet | Wang, Yichen Sarfraz, Irzam Pervaiz, Nida Hong, Rui Koga, Yusuke Akavoor, Vidya Cao, Xinyun Alabdullatif, Salam Zaib, Syed Ali Wang, Zhe Jansen, Frederick Yajima, Masanao Johnson, W. Evan Campbell, Joshua D. |
author_sort | Wang, Yichen |
collection | PubMed |
description | Analysis of single-cell RNA sequencing (scRNA-seq) data can reveal novel insights into the heterogeneity of complex biological systems. Many tools and workflows have been developed to perform different types of analyses. However, these tools are spread across different packages or programming environments, rely on different underlying data structures, and can only be utilized by people with knowledge of programming languages. In the Single-Cell Toolkit 2 (SCTK2), we have integrated a variety of popular tools and workflows to perform various aspects of scRNA-seq analysis. All tools and workflows can be run in the R console or using an intuitive graphical user interface built with R/Shiny. HTML reports generated with Rmarkdown can be used to document and recapitulate individual steps or entire analysis workflows. We show that the toolkit offers more features when compared with existing tools and allows for a seamless analysis of scRNA-seq data for non-computational users. |
format | Online Article Text |
id | pubmed-10436054 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-104360542023-08-19 Interactive analysis of single-cell data using flexible workflows with SCTK2 Wang, Yichen Sarfraz, Irzam Pervaiz, Nida Hong, Rui Koga, Yusuke Akavoor, Vidya Cao, Xinyun Alabdullatif, Salam Zaib, Syed Ali Wang, Zhe Jansen, Frederick Yajima, Masanao Johnson, W. Evan Campbell, Joshua D. Patterns (N Y) Descriptor Analysis of single-cell RNA sequencing (scRNA-seq) data can reveal novel insights into the heterogeneity of complex biological systems. Many tools and workflows have been developed to perform different types of analyses. However, these tools are spread across different packages or programming environments, rely on different underlying data structures, and can only be utilized by people with knowledge of programming languages. In the Single-Cell Toolkit 2 (SCTK2), we have integrated a variety of popular tools and workflows to perform various aspects of scRNA-seq analysis. All tools and workflows can be run in the R console or using an intuitive graphical user interface built with R/Shiny. HTML reports generated with Rmarkdown can be used to document and recapitulate individual steps or entire analysis workflows. We show that the toolkit offers more features when compared with existing tools and allows for a seamless analysis of scRNA-seq data for non-computational users. Elsevier 2023-08-03 /pmc/articles/PMC10436054/ /pubmed/37602214 http://dx.doi.org/10.1016/j.patter.2023.100814 Text en © 2023 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Descriptor Wang, Yichen Sarfraz, Irzam Pervaiz, Nida Hong, Rui Koga, Yusuke Akavoor, Vidya Cao, Xinyun Alabdullatif, Salam Zaib, Syed Ali Wang, Zhe Jansen, Frederick Yajima, Masanao Johnson, W. Evan Campbell, Joshua D. Interactive analysis of single-cell data using flexible workflows with SCTK2 |
title | Interactive analysis of single-cell data using flexible workflows with SCTK2 |
title_full | Interactive analysis of single-cell data using flexible workflows with SCTK2 |
title_fullStr | Interactive analysis of single-cell data using flexible workflows with SCTK2 |
title_full_unstemmed | Interactive analysis of single-cell data using flexible workflows with SCTK2 |
title_short | Interactive analysis of single-cell data using flexible workflows with SCTK2 |
title_sort | interactive analysis of single-cell data using flexible workflows with sctk2 |
topic | Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10436054/ https://www.ncbi.nlm.nih.gov/pubmed/37602214 http://dx.doi.org/10.1016/j.patter.2023.100814 |
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